LEADER 04985nam 2200673Ia 450 001 9910877709703321 005 20200520144314.0 010 $a1-281-93756-8 010 $a9786611937560 010 $a0-470-38277-5 010 $a0-470-38278-3 024 7 $a10.1002/9780470382776 035 $a(CKB)1000000000550404 035 $a(EBL)380554 035 $a(SSID)ssj0000123924 035 $a(PQKBManifestationID)11134077 035 $a(PQKBTitleCode)TC0000123924 035 $a(PQKBWorkID)10015722 035 $a(PQKB)11597121 035 $a(MiAaPQ)EBC380554 035 $a(CaBNVSL)mat05236612 035 $a(IDAMS)0b00006481094c83 035 $a(IEEE)5236612 035 $a(OCoLC)299046773 035 $a(PPN)185075770 035 $a(EXLCZ)991000000000550404 100 $a20080602d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aClustering /$fRui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsor 210 $aHoboken, N.J. $cWiley ;$aPiscataway, NJ : IEEE Press$dc2009 215 $a1 online resource (370 p.) 225 1 $aIEEE Press series on computational intelligence 300 $aDescription based upon print version of record. 311 $a0-470-27680-0 320 $aIncludes bibliographical references (p. 293-330) and indexes. 327 $aPREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classifi cation and Clustering -- 1.2. Defi nition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Defi nition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING. -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications--Genomic and Biological Sequence -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING. 327 $a9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX. 330 $aThis is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds. 410 0$aIEEE series on computational intelligence. 606 $aCluster analysis 606 $aMultivariate analysis 615 0$aCluster analysis. 615 0$aMultivariate analysis. 676 $a519.53 686 $a54.69$2bcl 700 $aXu$b Rui$0508234 701 $aWunsch$b Donald C$01760591 712 02$aIEEE Computational Intelligence Society. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877709703321 996 $aClustering$94199627 997 $aUNINA